System, method, and recording medium for alignment of backup and archival policy

ABSTRACT

A backup and archival policy method, system, and non-transitory computer readable medium, includes performing correlation analytics to determine identification of a backup policy aligned with a criticality of operational data and backup data including identifying low value backup data having a value less than a predetermined low value threshold, creating a one-time archival of the operational data and the backup data including the low value backup data, and removing the low value backup data from a future data protection policy.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation Application of U.S. patentapplication Ser. No. 15/226,347, filed on Aug. 2, 2016, the entirecontents of which are hereby incorporated by reference.

BACKGROUND

The present invention relates generally to a backup and archival policymethod, and more particularly, but not by way of limitation, to asystem, method, and recording medium for analyzing data under a backupsystem and subsequently performing correlation analytics on dataclassification results and a backup policy to transform data protectionfor an enterprise.

Ninety-percent of the data in the world was created in the last twoyears, and data volumes are rising faster than storage price isdeclining. Therefore, cheap storage is no longer the only answer tocontrolling the costs associated with data growth and backup policies.Data classification analytics can play an important role in discovering,recognizing, and subsequently acting on data in-place to transform amodem day enterprise to be data-driven by identifying relevant data.Conventional data classification processes help with finding the datathat matters eventually leading to the outcome of getting rid of old,obsolete data and identifying sensitive content.

However, data classification analysis has been conventionally limited tothe domain of operational data—from the domain of active file systems,active applications, such as E-mail, document management systems,content management systems, etc. Significant amount of similarirrelevant data gets accumulated in a data protection systemhistorically based on the backup and retention policy of the dataprotection system (it is noted that “backup” and “data protection” areused interchangeably and mean substantially the same in the context ofthis application).

Conventionally, backup of data or data protection is done because theuser wants to protect the enterprise from physical or online datacorruption. As an enterprise, a backup policy is specified such as adaily or weekly backup policy. The policy scans all the files to see ifa file was updated. If the file was updated, the policy creates a backupof the file and if the file was not updated, a backup is not created forthe file.

SUMMARY

In view of the above, the inventors have identified a technical problemin the conventional techniques that data classification does not analyzeand focus on the classification of data in the data protection workflowdue to the proprietary nature of the storage format of the dataprotection workflow. Also, a systematic analysis of backup policy needsto be implemented to the enterprise to de-clutter the backup system,reinforce to curtail the development of data debris in the backupsystem, and reduce the data center storage and networking costs incurredby data protection. Thus, the inventors have identified the technicalproblem with the conventional techniques that the techniques do not lookat a value of the data such as specific keywords or the like to find asensitivity of the file and does not look at the incremental behavior ofthe data based on archival rules (e.g., every back-up interval, the fileis scanned to see if the file was updated). As a result of the technicalproblem, the backup time is greatly increased and the backup procedureis costly.

Thus, the inventors have realized a technical solution to the technicalproblem by harnessing critical parameters obtained from infrastructureconfigurations and performance metrics, data relevance metrics from dataclassification analysis, and backup policy capturing backup-domain,associated backup policies, and retention policies to generate outputthat empowers an improved data protection workflow to support theautomated removal of data from the backup rotation (e.g., remove lessimportant or less updated data from being scanned to be backed-up),create a one-time archival of data and de-cluttering of the backuprotation (e.g., fix the backup plan and optimize past data), cleanup ofdata debris accumulated in the backup system, and to reduce backupstream costs by transforming the file-by-file network based backup forcertain data to controller-based replication.

In an exemplary embodiment, the present invention can provide a backupand archival policy method, the method including harnessing of metricsof data classification including both operational data and backup datafrom an end-to-end stack from a backup Information Lifecycle Governance(ILM) viewpoint.

Further, in another exemplary embodiment, the present invention canprovide a non-transitory computer-readable recording medium recording abackup and archival policy program, the program causing a computer toperform: harnessing of metrics of data classification including bothoperational data and backup data from an end-to-end stack from a backupInformation Lifecycle Governance (ILM) viewpoint.

Even further, in another exemplary embodiment, the present invention canprovide a backup and archival policy system, said system including aprocessor, and a memory, the memory storing instructions to cause theprocessor to: harness metrics of data classification including bothoperational data and backup data from an end-to-end stack from a backupInformation Lifecycle Governance (ILM) viewpoint.

There has thus been outlined, rather broadly, an embodiment of theinvention in order that the detailed description thereof herein may bebetter understood, and in order that the present contribution to the artmay be better appreciated. There are, of course, additional exemplaryembodiments of the invention that will be described below and which willform the subject matter of the claims appended hereto.

It is to be understood that the invention is not limited in itsapplication to the details of construction and to the arrangements ofthe components set forth in the following description or illustrated inthe drawings. The invention is capable of embodiments in addition tothose described and of being practiced and carried out in various ways.Also, it is to be understood that the phraseology and terminologyemployed herein, as well as the abstract, are for the purpose ofdescription and should not be regarded as limiting.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary aspects of the invention will be better understood fromthe following detailed description of the exemplary embodiments of theinvention with reference to the drawings.

FIG. 1 exemplarily shows a high-level flow chart for a backup andarchival policy method 100.

FIG. 2 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 3 depicts a cloud computing environment according to anotherembodiment of the present invention.

FIG. 4 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The invention will now be described with reference to FIGS. 1-4, inwhich like reference numerals refer to like parts throughout. It isemphasized that, according to common practice, the various features ofthe drawing are not necessarily to scale. On the contrary, thedimensions of the various features can be arbitrarily expanded orreduced for clarity. Exemplary embodiments are provided below forillustration purposes and do not limit the claims.

With reference now to FIG. 1, the backup and archival policy method 100includes various steps to harness critical parameter's from anend-to-end stack (e.g., infrastructure, data, and backup) from a backupInformation Lifecycle Governance (ILM) viewpoint, perform correlationanalytics to determine an identification of a backup policy aligned withthe business criticality of data, modify backup policy to align the dataprotection with the business relevance of data, and provide astandardized access interface for backup data similar to the operationdata. As shown in at least FIG. 2, one or more computers of a computersystem 12 can include a memory 28 having instructions stored in astorage system to perform the steps of FIG. 1.

With the use of these various steps and instructions, the backup andarchival policy method 100 may act in a more sophisticated and usefulfashion, and in a cognitive manner while giving the impression of mentalabilities and processes related to knowledge, attention, memory,judgment and evaluation, reasoning, and advanced computation. That is, asystem is said to be “cognitive” if it possesses macro-scaleproperties—perception, goal-oriented behavior, learning/memory andaction—that characterize systems (i.e., humans) that all agree arecognitive.

Although as shown in FIG(S). 2-4 and as described later, the computersystem/server 12 is exemplarily shown as one or more cloud computingnodes 10 of the cloud environment 50 as a general-purpose computingcircuit which may execute in a layer the backup and archival policysystem method (FIG. 3), it is noted that the present invention can beimplemented outside of the cloud environment.

The backup and archival policy method 100 receives a backup policy andretention rules from the data protection server 140 and a value of data(e.g., data relevance) from the data classification 130 of bothoperational data 110 and backup data 120.

Operational data comprises actively written and read data. For example,multiple applications can be run on a plurality of servers and theplurality of servers can use a certain amount of storage. Operationaldata comprises the day-to-day used data on a computer.

Backup data is different from the operational data in that the backupdata is the archived data from the operational data based on the backuppolicy and retention policy (e.g., backup rules). For example,operational data comprises the data on a “drive” on a personal computerwhile the backup data comprises the archived data of the drive based onan archival policy.

The data protection server 140 includes the backup policy and retentionpolicy of the backup data. For example, the backup policy or retentionpolicy can be rules such as excluded backup of the operating systemfiles or files older than a year, etc.

The data classification 130 includes classified data from thepartitioned data (e.g., the operational data 110 and the backup data120) based on a value of the data. The data is classified by making apointer of the two points to the data set, scans different files ofdifferent formats, looks for different rules, and figures out the filesthat contain the critical information based on keywords or a specificrule set by the user (e.g., copyrighted files, social security numbers,etc.). Any document that contains the particular keyword or triggers therule, is classified accordingly. Both the operational data and thebackup data is classified as part of the data classification 130 inputto the method 100.

Step 101 harnesses the metrics (e.g., critical parameters) of the dataclassification 130 from an end-to-end stack (e.g., from infrastructure,operational data 110, and backup data 120) from a backup InformationLifecycle governance (ILM) viewpoint. That is, Step 101 obtains theapplication metrics of how the application is laid out in theoperational data 110 such as application(s) (e.g., different e-mailapplications) to server (e.g., different servers for the e-mailapplications) to storage(s) (e.g., different types of storage for eache-mail) to block(s), tile(s), or object(s) and then identifies the valueof the data based on the data classification 130. Step 101 then does asimilar value identifying on the backup data 120 and then correlates thevalue of the operational data 110 and the backup data 120 by harnessingof the metrics from an end-to-end stack. Thus, Step 101 identifies theapplication, the classification of the data, the portion of the datathat is backed up, and how relevant the data is.

For example, each enterprise runs a different setup that needs to beharnessed front an end-to-end stack such as running in a cloudenvironment and each enterprise specifies the applications that they areusing metrics such as a first type of e-mail, a second type of e-mail,and a third type of e-mail, the type of storage available such as 200gigabytes, 200 terabytes, etc.

Step 101 harnesses the data from a value-to-value perspective andharnesses metrics such that Step 101 identifies the data, theapplication of the data, how much business value that the data has basedon a backup rule, what is the correlation of the data with respect tobackup (e.g., protection or no protection) such that the metrics areharnessed in an end-to-end stack perspective.

Step 102 performs correlated analytics to determine identification of abackup policy aligned with the business criticality of the data. Forexample, Step 102 crawls the operational data and if a predeterminedamount of data has debris based on the data classification (e.g., datahaving a low value) that is getting backed up based on an equal policywith data being significant that should be being backed up and Step 102correlates the information to align the backup policy with the businesscriticality of data. Further, Step 102 can cause a one-time backup ofthe data having debris to ensure that the data is protected and then nolonger backing up the data having debris.

In other words, Step 102 aligns the backup policy with the actual valueof the data such that the backup policy no longer includes the datahaving debris in the backup policy.

For example, a typical e-mail backup policy creates a backup of alle-mails within a particular time frame. Step 102 can align the backuppolicy of the e-mails with the business criticality of data so as to nolonger back up data having debris such as out of office e-mail responsesor similar e-mails with little value.

That is, Step 102 identifies the backup policy and correlates the backuppolicy with what has been done to the files in the past (e.g.,previously backed up files in the backup data 120). Therefore, evenwithout modifying the backup policy (as described in Step 103), Step 102can identify the backup policy to identify which data has no value(e.g., identify the backup value in terms of business value) and performa clean-up on the backup data 120 based on the identified backup policyas aligned with the business value (e.g., remove data in the backup data120 having no value to the business).

Step 102 identifies the value of each data of the data types (e.g., theoperation data 110 and the backup data 120) with the backup policy andperform a cleanup to filter the backup data 110 by deleting data thathas no value based on the backup policy to business criticality of thedata alignment, and also delete data that has already been backed up.

For example, if the backup policy has backed up “out of office e-mails”for the past five years, Step 102 identifies that the out of officee-mails have no value by performing correlation analytics to determineidentification of the backup policy aligned with the businesscriticality of data and then removes (deletes) all of the backup data120 for the out of office e-mails to “clean” the backup data, thereby,freeing up space for business relevant data in the future.

Step 103 modifies the identified backup policy to align the dataprotection (e.g., a future backup of data) of the backup policy with thebusiness relevance of data. In other words, Step 103 aligns the backuppolicy to appropriately backup data based on the value of the data forfuture backup operations. Thus, data having low value such as out ofoffice e-mails or data debris does not need to be scanned when thebackup is performed according to the backup policy which can greatlyreduce the amount of data needed to be backed up as well as the time tobackup by reducing the amount of data needed to be scanned to determineif the data was updated.

it is noted that Step 102 “cleans up” the backup data 120 currentlybacked up based on identifying the backup policy aligned with the valueof the data to the business and Step 103 modifies the backup policy suchthat all future backups align the value of the data with the business sothat the backup data 120 will not need to be “cleaned up” again. Inother words, Step 102 is preferably a one-time clean up because Step 103modifies the backup policy such that a clean-up would not need to beperformed again.

Thus, Step 102 filters through the data based on the identified value ofdata aligned with the backup policy to reduce the size of the backupdata 120 while Step 103 prevents the backup data 120 from including datawith no business value by modifying the backup policy.

Step 104 provides a standardized access interface for the backup data120 similar to the operational data 110 interface. For example,operation data 110 is easily accessible (e.g., such as using Windows™ orLinux™) but backup data 120 does not have a standardized accessinterface to view the backup data 120. For example, standard Notes®database (NSF) to Tivoli Storage Manager (TSM) can be provided.

Thus, the steps of the method 100 generate an output that empowers anenterprise with actionable insights to support the automated removal ofdata from the backup rotation, perform a one-time archival of data andde-cluttering of the backup rotation (e.g., identified data created onceand will not change such that the data does not need to be re-archivedevery time in the backup policy), clean-up data debris accumulated inthe backup data, and reduce backup stream costs by transforming thefile-by-file network based on backup for certain data tocontroller-based replication (e.g., Step 102 can identify when thebackup policy is no longer efficient because of the amount of dataneeded to be backed up).

Exemplary Hardware Aspects, Using a Cloud Computing Environment

Although this detailed description includes an exemplary embodiment ofthe present invention in a cloud computing environment, it is to beunderstood that implementation of the teachings recited herein are notlimited to such a cloud computing environment. Rather, embodiments ofthe present invention are capable of being implemented in conjunctionwith any other type of computing environment now known or laterdeveloped.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country; state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models arc as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client circuits through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 2, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop circuits, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or circuits, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingcircuits that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage circuits.

As shown in FIG. 2, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing circuit. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externalcircuits 14 such as a keyboard, a pointing circuit, a display 24, etc.;one or more circuits that enable a user to interact with computersystem/server 12; and/or any circuits (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing circuits. Such communication can occur Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,circuit drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 3, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing circuits used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingcircuit. It is understood that the types of computing circuits 54A-Nshown in FIG. 3 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized circuit over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 4, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 3) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 4 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage circuits 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, more particularly relative to thepresent invention, the backup and archival policy method 100 describedherein.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claimelements, and no amendment to any claim of the present applicationshould be construed as a disclaimer of any interest in or right to anequivalent of any element or feature of the amended claim.

What is claimed is:
 1. A backup and archival policy method, the methodcomprising: performing correlation analytics to determine identificationof a backup policy aligned with a criticality of operational data andbackup data including identifying low value backup data having a valueless than a predetermined low value threshold; creating a one-timearchival of the operational data and the backup data including the lowvalue backup data; and removing the low value backup data from a futuredata protection policy.
 2. A non-transitory computer-readable recordingmedium recording a backup and archival policy program, the programcausing a computer to perform: performing correlation analytics todetermine identification of a backup policy aligned with a criticalityof operational data and backup data including identifying low valuebackup data having a value less than a predetermined low valuethreshold; creating a one-time archival of the operational data and thebackup data including the low value backup data; and removing the lowvalue backup data from a future data protection policy.
 3. A backup andarchival policy system, said system comprising: a processor; and amemory, the memory storing instructions to cause the processor toperform; performing correlation analytics to determine identification ofa backup policy aligned with a criticality of operational data andbackup data including identifying low value backup data having a valueless than a predetermined low value threshold; creating a one-timearchival of the operational data and the backup data including the lowvalue backup data; and removing the low value backup data from a futuredata protection policy.